This paper presents work on vision based robotic grasping. The proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. We evaluate and compare the performance of linear and non-linear classifiers. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.

Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.

For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking
while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom
lightweight systems, conventional identification of rigid body dynamics models using CAD data and
actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method
is data-driven parameter estimation, but significant noise in measured and inferred variables affects it
adversely. Moreover, standard estimation procedures may give physically inconsistent results due to
unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing
a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor
Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm
that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and
output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems,
achieving an error of up to three times lower than other state-of-the-art machine learning methods.

In this work we present the ﬁrst constrained stochastic op-
timal feedback controller applied to a fully nonlinear, tendon
driven index ﬁnger model. Our model also takes into account an
extensor mechanism, and muscle force-length and force-velocity
properties. We show this feedback controller is robust to noise
and perturbations to the dynamics, while successfully handling
the nonlinearities and high dimensionality of the system. By ex-
tending prior methods, we are able to approximate physiological
realism by ensuring positivity of neural commands and tendon
tensions at all timesthus can, for the ﬁrst time, use the optimal control framework
to predict biologically plausible tendon tensions for a nonlinear
neuromuscular ﬁnger model.
METHODS
1 Muscle Model
The rigid-body triple pendulum ﬁnger model with slightly
viscous joints is actuated by Hill-type muscle models. Joint
torques are generated by the seven muscles of the index ﬁn-

We present a novel algorithm for efficient learning and feature selection in high-
dimensional regression problems. We arrive at this model through a modification of
the standard regression model, enabling us to derive a probabilistic version of the
well-known statistical regression technique of backfitting. Using the Expectation-
Maximization algorithm, along with variational approximation methods to overcome
intractability, we extend our algorithm to include automatic relevance detection
of the input features. This Variational Bayesian Least Squares (VBLS) approach
retains its simplicity as a linear model, but offers a novel statistically robust â??black-
boxâ? approach to generalized linear regression with high-dimensional inputs. It can
be easily extended to nonlinear regression and classification problems. In particular,
we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector
Machine, with VBLS at its core, offering significant computational and robustness
advantages for this class of methods. We evaluate our algorithm on synthetic and
neurophysiological data sets, as well as on standard regression and classification
benchmark data sets, comparing it with other competitive statistical approaches
and demonstrating its suitability as a drop-in replacement for other generalized
linear regression techniques.

In the proceedings of American Control Conference (ACC 2010) , 2010, clmc (article)

Abstract

We present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state- and control-dependent process noise, and proceed to derive the second-order expansion of the cost-to-go. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

In a not too distant future, robots will be a natural part of
daily life in human society, providing assistance in many
areas ranging from clinical applications, education and care
giving, to normal household environments [1]. It is hard to
imagine that all possible tasks can be preprogrammed in such
robots. Robots need to be able to learn, either by themselves
or with the help of human supervision. Additionally, wear and
tear on robots in daily use needs to be automatically compensated
for, which requires a form of continuous self-calibration,
another form of learning. Finally, robots need to react to stochastic
and dynamic environments, i.e., they need to learn
how to optimally adapt to uncertainty and unforeseen
changes. Robot learning is going to be a key ingredient for the
future of autonomous robots.
While robot learning covers a rather large field, from learning
to perceive, to plan, to make decisions, etc., we will focus
this review on topics of learning control, in particular, as it is
concerned with learning control in simulated or actual physical
robots. In general, learning control refers to the process of
acquiring a control strategy for a particular control system and
a particular task by trial and error. Learning control is usually
distinguished from adaptive control [2] in that the learning system
can have rather general optimization objectivesâ??not just,
e.g., minimal tracking errorâ??and is permitted to fail during
the process of learning, while adaptive control emphasizes fast
convergence without failure. Thus, learning control resembles
the way that humans and animals acquire new movement
strategies, while adaptive control is a special case of learning
control that fulfills stringent performance constraints, e.g., as
needed in life-critical systems like airplanes.
Learning control has been an active topic of research for at
least three decades. However, given the lack of working robots
that actually use learning components, more work needs to be
done before robot learning will make it beyond the laboratory
environment. This article will survey some ongoing and past
activities in robot learning to assess where the field stands and
where it is going. We will largely focus on nonwheeled robots
and less on topics of state estimation, as typically explored in
wheeled robots [3]â??6], and we emphasize learning in continuous
state-action spaces rather than discrete state-action spaces [7], [8].
We will illustrate the different topics of robot learning with
examples from our own research with anthropomorphic and
humanoid robots.

We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing
it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization, and control techniques
to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal
foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-
Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force
control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller
by testing it on the LittleDog quadruped robot, over a wide variety of rough terrains of varying difficulty levels. The
terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length
of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by
an independent external test team on terrain that has never been shown to us.

2007

Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can accomplish a multitude of different tasks, triggered by environmental context or higher level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning and human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error.
However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains.
In this thesis, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.
As a theoretical foundation, we first study a general framework to generate control laws for real robots with a particular focus on skills represented as dynamical systems in differential constraint form. We present a point-wise optimal control framework resulting from a generalization of Gauss' principle and show how various well-known robot control laws can be derived by modifying the metric of the employed cost function. The framework has been successfully applied to task space tracking control for holonomic systems for several different metrics on the anthropomorphic SARCOS Master Arm.
In order to overcome the limiting requirement of accurate robot models, we first employ learning methods to find learning controllers for task space control.
However, when learning to execute a redundant control problem, we face the general problem of the non-convexity of the solution space which can force the robot to steer into physically impossible configurations if supervised learning methods are employed without further consideration. This problem can be resolved using two major insights, i.e., the learning problem can be treated as locally convex and the cost function of the analytical framework can be used to ensure global consistency. Thus, we derive an immediate reinforcement learning algorithm from the expectation-maximization point of view which leads to a reward-weighted regression technique. This method can be used both for operational space control as well as general immediate reward reinforcement learning problems. We demonstrate the feasibility of the resulting framework on the problem of redundant end-effector tracking for both a simulated 3 degrees of freedom robot arm as well as for a simulated anthropomorphic SARCOS Master Arm.
While learning to execute tasks in task space is an essential component to a general framework to motor skill learning, learning the actual task is of even higher importance, particularly as this issue is more frequently beyond the abilities of analytical approaches than execution. We focus on the learning of elemental tasks which can serve as the "building blocks of movement
generation", called motor primitives. Motor primitives are parameterized task representations based on splines or nonlinear differential equations with desired attractor properties. While imitation learning of parameterized motor primitives is a relatively well-understood problem, the self-improvement by interaction of the system with the environment remains a challenging problem, tackled in the fourth chapter of this thesis.
For pursuing this goal, we highlight the difficulties with current
reinforcement learning methods, and outline both established and novel
algorithms for the gradient-based improvement of parameterized
policies. We compare these algorithms in the context of motor
primitive learning, and show that our most modern algorithm, the
Episodic Natural Actor-Critic outperforms previous algorithms by at
least an order of magnitude. We demonstrate the efficiency of this
reinforcement learning method in the application of learning to hit a
baseball with an anthropomorphic robot arm.
In conclusion, in this thesis, we have contributed a general framework for analytically computing robot control laws which can be used for deriving various previous control approaches and serves as foundation as well as inspiration for our learning algorithms. We have introduced two classes of novel reinforcement learning methods, i.e., the Natural Actor-Critic and the Reward-Weighted Regression algorithm. These algorithms have been used in order to replace the analytical components of the theoretical framework by learned representations. Evaluations have been performed on both simulated and real robot arms.

HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

Abstract

Research in robotics has moved away from its primary focus on industrial
applications. The New Robotics is a vision that has been developed in past years
by our own university and many other national and international research
instiutions and addresses how increasingly more human-like robots can live
among us and take over tasks where our current society has shortcomings. Elder
care, physical therapy, child education, search and rescue, and general
assistance in daily life situations are some of the examples that will benefit from
the New Robotics in the near future. With these goals in mind, research for the
New Robotics has to embrace a broad interdisciplinary approach, ranging from
traditional mathematical issues of robotics to novel issues in psychology,
neuroscience, and ethics. This paper outlines some of the important research
problems that will need to be resolved to make the New Robotics a reality.

In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems